Many authors have pointed out the inconsistencies between popular XAI tools, raising questions as to which method is more reliable in particular cases [1]}, [2]}, [3]}. Theoretical foundations have proven elusive in this area, perhaps due to the perceived subjectivity inherent to notions such as “intelligible” and “relevant” [4]}. Practitioners often seek refuge in the axiomatic guarantees of Shapley values, which have become the de facto standard in many XAI applications, due in no small part to their attractive theoretical properties [5]}. However, ambiguities regarding the underlying assumptions of the method [6]} and the recent proliferation of mutually incompatible implementations [7]}, [8]} have complicated this picture. Despite the abundance of alternative XAI tools [9]}, a dearth of theory persists. This has led some to conclude that the goals of XAI are underspecified [10]}, and even that post-hoc methods do more harm than good [11]}
